Real Datasets: We compare the models on the USCA312 dataset, of distances between North American cities, reported in previous work [1]}, and on bio-diseasome, a network of human disorders and diseases with reference to their genetic origins [2]}, on Table REF .
On the USCA312 dataset, Hermitian symmetric spaces perform on par with the compared target manifolds. For the bio-diseasome dataset, the Siegel model outperforms all baselines. This shows the strong reconstruction capabilities of Hermitian symmetric spaces for real-world data as well. It also indicates that vertices in bio-diseasome form a network with a more intricate geometry, which the matrix model is able to unfold to a better extent. The possibilities of leveraging graph embeddings into Hermitian symmetric spaces for structural analysis of networks will be explored in future work.
